A two-stage mobile robot localization method by overlapping segment-based maps

This paper presents a new method for accurately estimating the pose (position and orientation) of a mobile robot by registering a segment-based local map observed from the current robot pose and a global map. The method works in a two-stage procedure. First, the orientation is determined by aligning the local and global map through a voting process based on a generalized Hough transform. Second, it uses a coarse-to-fine approach for selecting candidate positions and a weighted voting scheme to determine the degree of overlap of the two maps at each of these poses. Unlike other methods previously proposed, this approach allows us to uncouple the problem of estimating the robot orientation and the robot position which may be useful for some applications. In addition it can manage environments described by many (possibly short) segments. This paper presents some experimental results based on our mobile robot RAM-2 that show the accuracy and the robustness of the proposed method even for poor quality maps and large dead-reckoning errors.